WiSLAT: A Simultaneous Device Localization and Target Tracking Method for Wi-Fi Systems

TL;DR

WiSLAT introduces a novel Wi-Fi-based method that jointly localizes devices and tracks targets without prior system information by leveraging Doppler frequency detection and a specialized optimization algorithm.

Contribution

It presents a new joint localization and tracking approach that eliminates the need for prior system information, using Doppler frequency analysis and a low-complexity optimization algorithm.

Findings

Median trajectory-tracking error of 0.68 meters in indoor experiments.

Effective joint localization and tracking without prior anchor information.

Utilizes Doppler frequencies for simultaneous device and target localization.

Abstract

It has been shown that the channel state information (CSI) of a Wi-Fi system can be exploited to localize Wi-Fi devices or track trajectory of a moving target. In the existing literature, both sensing tasks are treated separately and some prior information is usually requested, including the signal fingerprints, the locations of some anchor devices in the Wi-Fi system, and etc. In the proposed WiSLAT method, however, it is shown that both sensing tasks can assist each other, such that the request on prior system information can be eliminated. Particularly, in a Wi-Fi system with an access point (AP) and at least three stations, where the locations of the stations are unknown, the WiSLAT is designed to detect the Doppler frequencies of the downlink CSI at the stations, such that their locations and the trajectory of the target with respect to the AP can be inferred. The joint detection can be conducted by searching the optimal stations' locations and target's trajectory, such that their corresponding Doppler frequencies fit the observed ones best. Due to the tremendous non-convex search space, a low-complexity sub-optimal algorithm integrating alternate optimization, extended Kalman filter and density-based clustering is proposed in WiSLAT. Experiments conducted in indoor environments demonstrate the effectiveness of WiSLAT, achieving a median trajectory-tracking error of 0.68 m.